Diesel Particulate Filter Dynamic Filtration

Particulate matter (PM) emitted from diesel vehicles has serious threats to the global climate, environment, and human health. Diesel particulate filters (DPFs) are currently the only effective after-treatment technology for reducing PM emissions, making it crucial to evaluate their filtration and pressure drop performance using numerical methods. This study developed and validated a lumped model based on spherical packed bed theory and Darcy's law to describe the dynamic PM filtration process in DPFs. The model accurately predicts real-time characteristics such as wall porosity, soot load, and pressure drop. Additionally, it examines the influence of channel characteristics, monolithic size, and exhaust conditions on filtration and pressure drop performance. Results indicate that increasing cell density, wall thickness, DPF length, and diameter enhances filtration efficiency, while higher DPF inlet temperatures and exhaust mass flow rates negatively impact performance. Moreover, wall thickness, DPF diameter, and exhaust flow rate significantly affect total pressure drop. These findings enhance dynamic performance prediction in PM filtration and provide a theoretical foundation for optimizing DPF structures.